Comparison of 3D Printing and Traditional Hand Orthosis Fabrication
Bibliographic record
Abstract
Traditional methods of custom orthosis fabrication are prone to challenges and limitations. Three-dimensional (3D) printing has been piloted with lower extremity orthotics and worthy of exploration with upper extremities. The aim of this study was to compare three-dimensionally printed wrist immobilization splints to conventionally made orthoses in terms of fabrication, comfort, and functionality. Three healthy participants with no history of wrist or hand conditions were recruited to be fitted for conventional and 3D-printed wrist immobilization splints. A sequential mixed-methods study design was conducted to explore comfort, fabrication, and functionality. An ethnographic study was conducted afterward to further understand the fabrication process of 3D-printed orthotics. The Quebec User Evaluation of Satisfaction with Assistive Technology and a Splint Analysis form was used to assess comfort. The function was assessed using the Jebsen-Taylor Hand Function Test. A five-point satisfaction Likert scale was used to evaluate fabrication. Although the results were not statistically significant due to the small sample size, 3D-printed orthotics appear to provide some benefits over traditional methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".